One of the key problems in developing Enhanced and Synthetic Vision Systems is evaluating their effectiveness in enhancing human visual performance. A validated simulation of human vision would provide a means of avoiding costly and time-consuming testing of human observers. We describe an image-based simulation of human visual search, detection, and identification, and efforts to further validate
and refine this simulation. One of the advantages of an image-based simulation is that it can predict performance for exactly the same visual stimuli seen by human operators. This makes it possible to assess aspects of the imagery, such as particular types and amounts of background clutter and sensor distortions, that are not usually considered in non-image based models. We present two validation studies - one showing that the simulation accurately predicts human color discrimination, and a second showing that it produces probabilities of detection (Pd's) that closely match Blackwell-type human threshold data.